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The exposure to chemical pesticides is one of the world's concerns, especially in Vietnam, which is becoming a key player in global agriculture. The chronic long-term exposure to pesticides, especially organophosphorus groups poses increased health risks such as cancer and related diseases due to their hazardous metabolites. The characterization of urinary pesticides is essential to understand the pesticide exposure patterns. Therefore, this research aims to develop a liquid chromatography–tandem mass spectrometry procedure for the quantification of six dialkyl phosphate metabolites of organophosphorus pesticides based on simulated human urine containing dialkyl phosphates and urine samples of organophosphorus pesticides-exposed farmers in An Giang province of Vietnam. As a result, a highly sensitive procedure with a negative ion atmospheric pressure chemical ionization source, fosfomycin as internal standard and multireaction monitoring, was successfully validated in compliance with international guidelines for simultaneous quantitative determination of six dialkyl phosphates in human urine samples. Molecular and fragmented ions for quantification were consistent with the standard spectrum. The linear ranges of DMP, DEP, DMTP, DETP, DMDTP, and DEDTP were 5.29–1000.58, 5.10–1000.19, 5.10–1000.20, 5.06–1000.11, 5.06–1000.11, 5.30–1000.60, and 5.06–1000.12 ng/mL, respectively. The validation results showed that the selectivity, intraday and interday precision and accuracy, matrix effect, carry over, dilution, and stability of all the analytes were in the acceptable range. In total, 383 spot urine samples from people working with pesticides were satisfactorily analyzed by the proposed procedure. Over 80% of farmers were detected with at least one organophosphate metabolite, especially DEDTP with high concentrations, up to 5015.0 ng/mL, which alerts the high likelihood of pesticide exposure in the community of rural areas in Vietnam.

Danzhi Jiangtang capsule (DJC) is a traditional Chinese medicine prescription that has been clinically used to treat Type 2 diabetes mellitus and its complications. However, research on the chemical compounds present in DJC remains limited. In this study, an analytical strategy based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was developed for the rapid and systematic characterization of chemical compounds in DJC. Firstly, a DJC self-built database was established, and UPLC-Q-TOF/MS was applied for comprehensive profiling of DJC's chemical compounds. Then, R language combined with MZmine was used for data preprocessing to construct the ion information list and extract effective data. Finally, the compounds were identified by multiple data processing techniques (multiple-point screening mass defect filtering [MDF], extracted ion chromatogram [EIC], neutral loss filter [NLF], diagnostic fragment ion filtering [DFIF], and direct identification method [including retention time, fragment behavior and reference substances]). Eventually, 137 compounds were characterized from DJC, including 19 monoterpenoids, 26 triterpenoids, 8 flavonoids, 12 iridoids, 7 phenylethanoid glycosides, 8 acetophenones, 23 organic acids, 2 violet ketones, 13 cyclic peptides, 8 alkaloids, 2 fatty acids, and 9 other compounds. Among these, 16 compounds were verified using reference substances. The study indicated that the analytical strategy established in this study effectively supports the in-depth study of DJC's chemical constituents and provides essential data for subsequent in vivo studies.

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Pulmonary fibrosis (PF) is a chronic and progressive lung disease with fatal consequences. The study of PF is challenging due to the complex mechanism involved, the need to understand the heterogeneity and spatial organization within lung tissues. In this study, we investigate the metabolic heterogeneity between two forms of lung fibrosis: idiopathic pulmonary fibrosis (IPF) and silicosis, using advanced spatially-resolved metabolomics techniques. Employing high-resolution mass spectrometry imaging, we spatially mapped and identified over 260 metabolites in lung tissue sections from mouse models of IPF and silicosis. Histological analysis confirmed fibrosis in both models, with distinct pathological features: alveolar destruction and collagen deposition in IPF, and nodule formation in silicosis. Metabolomic analysis revealed significant differences between IPF and silicosis in key metabolic pathways, including phospholipid metabolism, purine/pyrimidine metabolism, and the TCA cycle. Notably, phosphocholine was elevated in silicosis but reduced in IPF, while carnitine levels decreased in both conditions. Additionally, glycolytic activity was increased in both models, but TCA cycle intermediates showed opposing trends. These findings highlight the spatial metabolic heterogeneity of PF and suggest potential metabolic targets for therapeutic intervention. Further investigation into the regulatory mechanisms behind these metabolic shifts may open new avenues for fibrosis treatment.

In the current scenario, herbal raw materials are identified via morphotaxonomy, microscopic pharmacognosy, or DNA barcoding. However, these methods do not reveal their chemical integrity, while plant raw materials play a crucial role in the quality of plant-based medicine. To overcome this limitation, we used a mass spectrometry-based method to identify 30 botanicals. This assay followed a standard operating procedure (SOP) from sample preparation to the reference library's mass spectrum fingerprint (MSFP) search. The MS1 score showed a similarity index between the input data and the reference mass spectrum. A more than 50% MS1 score was the critical threshold for accurately identifying botanicals based on their chemical integrity. Interestingly, the analysis of 30 different plant species yielded no false results. The results were 100% accurate and selective for tested botanical samples. However, we found that the standard deviation of analytical assays and biological replicates was ± 3.5 and ± 6.3 (MS1 score) for all analyzed samples, respectively. Intraspecies variability showed MS1 scores > 50% ± 10, whereas interspecies variability was observed with MS1 scores < 50% ± 10. The MS1 score was observed, dependent on the plant species, ranging from 53.00% (± 2.65) to 89.76% (± 4.08). In addition, the method was tested to see how seasonal and geographical changes affected search results. The MS1 score changed by less than 15%. We simultaneously created a chemical barcode (unique molecular weight sequence) for each plant species to validate search results and ensure the reliable identification of botanicals.

Sulfides are ubiquity in atmosphere and can convert to be H2SO3/H2SO4, which could affect the inflammatory responses induced by ozone. However, the mixing effect and mechanism of H2SO3/H2SO4 and ozone at molecular-level on the lung surface is still indefinable. Herein, using 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylglycerol (POPG) monolayer as a model, effects of H2SO4/H2SO3 on interfacial ozonolysis of phospholipids were explored. Both H2SO4 and H2SO3 could decrease ozonolysis efficiencies of POPG and showed a remarkable concentration dependence. The main components of H2SO4 and H2SO3 in investigated system and their effects on POPG ozonolysis were separately explored, and the mechanism was proposed. The observed decrease of ozonolysis efficiencies resulted from POPG hydrolysis induced by H+ and reactive activity of HSO3 − and SO3 2− towards ozone. The hydrolysis of POPG could provide oleic acids, which further lowered the ozonolysis efficiency. In addition, the efficiency of POPG ozonolysis in H2SO3 case was lower than that in H2SO4 case, and the self-sacrificing oxidation of HSO3 − and SO3 2− by ozone was responsible for this process. Considering extra phospholipids in epithelial lining fluid of lung, the short-term or low concentration exposure of H2SO4/H2SO3 was thought to trigger the self-protection of lung. However, the long-term or high concentration exposure would lead to irreversible damage.

Mass spectrometry-based proteomics is essential for advancing preventive and personalised medicine. Technological advancements have greatly increased both the number and sensitivity of spectra generated in a single experiment. Traditionally, spectra are identified using database search engines that depend on large and continuously expanding databases. This expansion enlarges the search space, posing challenges for controlling the false discovery rate in peptide identification. While many bioinformatic workflows employ rescoring algorithms as a post-processing step to manage false discoveries, preprocessing spectra offers a promising alternative. One such method, spectral quality assessment, classifies spectra as “high” quality (likely containing a peptide) or “low” quality (predominantly consisting of noise). This review provides a comprehensive perspective on spectral quality assessment, examining existing tools and their underlying principles. We discuss key considerations such as the definition of spectral quality, normalisation, the use of experimental training data, and future research in the field. By highlighting the potential of spectral quality assessment to improve peptide identification and reduce false discoveries, we aim to elaborate on its potential for the proteomics community.

Secondary electrospray ionization (SESI) and extractive electrospray ionization (EESI), as derivative technologies of electrospray ionization (ESI), have empowered the real-time analysis of trace compounds residing in gases and aerosols. Over the past three decades, SESI and EESI have demonstrated remarkable potential in a wide spectrum of applications, spanning disease diagnosis, drug detection, food safety, and environmental surveillance. Concurrently, the strides made in deciphering the ionization mechanisms of SESI and EESI have spurred the creation of diverse ion source configurations that are characterized by enhanced sensitivity and diminished background noise. This comprehensive review encapsulates the ionization mechanisms inherent in SESI and EESI processes, with particular emphasis on the impact of analyte characteristics (such as proton affinity, dipole moment, polarizability, and solubility) and ion source operational parameters (encompassing temperature, humidity, voltage, flow rate and electrospray composition) on ionization efficiency. Additionally, it delves into the progression of SESI and EESI sources, highlights recent breakthroughs, and probes into future trajectories, furnishing novel perspectives for the development of both technologies and the associated instruments.

Polo-like kinase 1 (Plk1) is a serine/threonine kinase involved in regulating the cell cycle. It is activated by aurora kinase B along with the cofactors Borealin, INCE, and survivin. Plk1 is involved in the development of resistances to chemotherapeutics such as doxorubicin, Taxol, and gemcitabine. It has been shown that patients with higher levels of Plk1 have lower survival rates. Onvansertib is a competitive ATP inhibitor for Plk1 in clinical trials for the treatment of tumors and has recently entered a trial for the treatment of KRAS mutant colorectal cancers (CRCs). In this study, we conducted an untargeted liquid chromatography–mass spectrometry (LC–MS) proteomics study as well as an untargeted lipidomics analysis of HCT 116 spheroids treated with onvansertib over a 72-h treatment time-course experiment. Mass spectrometry imaging (MSI) showed that onvansertib begins to accumulate most prominently after 12 h of treatment and continues to accumulate through 72 h. Proteomic results displayed alterations to cell cycle control proteins and an increasing abundance of aurora kinase B and Borealin. The proteomics data also showed alterations to many lipid metabolism enzymes. The MSI lipidomics data indicated alterations to phosphatidylcholine lipids, with many lipids increasing in abundance over time or increasing until 12 h of onvansertib treatment and decreasing after that time point. In summary, these results suggest that onvansertib is causing cells within the spheroid to halt at a certain phase of the cell cycle in accordance with previous literature. Our findings suggest the S phase is likely interrupted, with observed alterations in cell cycle control proteins and PC lipid abundance.

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Dental caries, a prevalent global infectious condition affecting over 95% of adults, remains elusive in its precise etiology. Addressing the complex dynamics of caries demands a thorough exploration of taxonomic, potential, active, and encoded functions within the oral ecosystem. Metabolomic profiling emerges as a crucial tool, offering immediate insights into microecosystem physiology and linking directly to the phenotype. Identified metabolites, indicative of caries status, play a pivotal role in unraveling the metabolic processes underlying the disease. Despite challenges in metabolite variability, the use of metabolomics, particularly via mass spectrometry and nuclear magnetic resonance spectroscopy, holds promise in caries research. This review comprehensively examines metabolomics in caries prevention, diagnosis, and treatment, highlighting distinct metabolite expression patterns and their associations with disease-related bacterial communities. Pioneering in approach, it integrates singular and combinatory metabolomics methodologies, diverse biofluids, and study designs, critically evaluating prior limitations while offering expert insights for future investigations. By synthesizing existing knowledge, this review significantly advances our comprehension of caries, providing a foundation for improved prevention and treatment strategies.

Benefits of miniaturized chromatography with various detection modes, such as increased sensitivity, chromatographic efficiency, and speed, were recognized nearly 50 years ago. Over the past two decades, this approach has experienced rapid growth, driven by the emergence of mass spectrometry applications serving -omics sciences and the need for analyzing minute volumes of precious samples with ever higher sensitivity. While nanoscale liquid chromatography (flow rates <1 μL/min) has gained widespread recognition in proteomics, the adoption of microscale setups (flow rates ranging from 1 to 100 μL/min) for low molecular weight compound applications, including metabolomics, has been surprisingly slow, despite the inherent advantages of the approach. Highly heterogeneous matrices and chemical structures accompanied by a relative lack of options for both selective sample preparation and user-friendly equipment are usually reported as major hindrances. To facilitate the wider implementation of microscale analyses, we present here a comprehensive tutorial encompassing important theoretical and practical considerations. We provide fundamental principles in micro-chromatography and guide the reader through the main elements of a microflow workflow, from LC pumps to ionization devices. Finally, based on both our literature overview and experience, illustrated by some in-house data, we highlight the critical importance of the ionization source design and its careful optimization to achieve significant sensitivity improvement.

Liquid chromatography paired with tandem mass spectrometry (LC-MS/MS) is the gold standard in measurement of endocannabinoid concentrations in biomatrices. We conducted a systematic review of literature to identify advances in targeted LC-MS/MS methods in the period 2017–2024. We found that LC-MS/MS methods for endocannabinoid quantification are relatively consistent both across time and across biomatrices. Recent advances have primarily been in three areas: (1) sample preparation techniques, specific to the chosen biomatrix; (2) the range of biomatrices tested, recently favoring blood matrices; and (3) the breadth of endocannabinoid and endocannabinoid-like analytes incorporated into assays. This review provides a summary of the recent literature and a guide for researchers looking to establish the best methods for quantifying endocannabinoids in a range of biomatrices.

Oral fluid sampling offers advantages over other biological matrices, mainly due to its noninvasive procedure avoiding privacy intrusion. The fully automated sample preparation procedure is based on salting-out assisted liquid–liquid extraction (SALLE) combined with high-efficiency LC-MS/MS methods for both screening and confirmation of 37 drugs and incorporates novel features enabling direct injection of acetonitrile extracts into an innovative chromatographic system. The methods' drug panel includes opioids, benzodiazepines, benzodiazepine-like drugs, cannabinoids, and stimulants. A full method validation was performed using OF/buffer from Greiner Bio-ONE International and Quantisal saliva collection devices. The validation included assessments of linearity, sensitivity, precision, accuracy, extraction recovery, matrix effects, process efficiency, stability, and carryover. All compounds demonstrated linearity across the concentration range 1–25 ng/mL, with R 2 ≥ 0.99. Both methods' limit of detection ranged between 0.001 and 0.03 ng/mL, and the limit of quantification ranged between 0.02 and 0.09 ng/mL. Precision was ≤ 14.8% for screening and ≤ 8.5% for the confirmation method. Accuracy was ± 13.6% for screening and ± 8.7% (except at 0.5 and 1 ng/mL, where it was ± 25.3% and ± 17.6%, respectively) for the confirmation method. Extraction recoveries ranged from 40.0% to 95.1%, except for hydromorphone (27.4%) and morphine (34.4%). Although matrix effects were observed for a large number of compounds to varying degrees, they were largely compensated for by the use of deuterium- and 13C-labeled internal standards (IS). IS-corrected overall process efficiency ranged from 100.7% to 119.1% with precision (CV%) ≤ 10.8% for all compounds. Spiked calibrators and QC samples in OF were stable in autosampler for up to 72 h and in the freezer for 3 days. Methanol working solutions were stable for 6 months. No significant carryover was observed. The methods have been successfully implemented in the routine analysis of approximately > 1000 samples per month since March 2024.

Direct analysis of aromatic glycosidic precursors in plants has posed an analytical challenge for decades. Traditional techniques, such as SPE-GC/MS, primarily provided information on volatile aglycones released through hydrolysis. However, the application of high-resolution mass spectrometry combined with liquid chromatography has enabled the direct analysis of intact glycosides without the need for derivatization or hydrolysis. Advances in soft ionization methods, such as DART, offer a novel approach to exploring the hidden aromatic potential in grapes without chromatographic separation. In this work, we present a novel and rapid method for screening aromatic glycosidic precursors in white grapes using high-resolution mass spectrometry (Orbitrap) combined with the soft ionization DART method with nitrogen plasma. Optimization of N2-DART ionization parameters, including grid voltage, gas temperature, and Dip-it sampler speed, performed on selected synthetic glycosidic precursors, allowed the establishment of characteristic ionization patterns and evaluation of 15 groups of glycosidic precursors. The results from the profiling analysis using the N2-DART-Orbitrap-MS method are comparable to those obtained by HPLC/Orbitrap-MS method. This new analytical approach, N2-DART-Orbitrap-MS, reduces drastically analysis time by eliminating the need for chromatographic separation when screening glycoside precursors, uses a convenient Dip-it tips for sampling. It also allows for deeper exploration of ionization using nitrogen plasma, applied for the first time in the analysis of glycoside precursors, demonstrating the applicability of this method for the rapid characterization and screening of glycosidically bound aroma compounds in plants.

The ion collision cross section (CCS) is closely related to the structural and physical conformation of compounds, making it an ideal parameter for constructing databases. In recent years, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has gained widespread application in CCS measurements thanks to its ultra-high mass resolution and accuracy. Due to the collisions between ions and neutral molecules within the FT-ICR MS cell, the image current decays. Based on this feature, the ion CCS can be precisely calculated by applying corresponding collision models and algorithms. A new time-frequency analysis method is introduced: the flipping-filtering method based on the Levenberg–Marquardt algorithm. Before filtering, the data undergoes flipping and extension, effectively mitigating the issue of signal point waste commonly associated with traditional filtering techniques. This approach ensures a smooth and uninterrupted envelope of the image current signal while significantly enhancing the accuracy of decay factor and ion CCS measurements. Compared to the linewidth correction method and the line shape fitting method, this method exhibits superior noise resistance and resolution capabilities, serving as a valuable adjunct to ion CCS measurement techniques.

Because the manual interpretation of ESI-MS n fragmentation spectra is time-consuming and usually requires expert knowledge, automated annotation is often sought. The fragmentation software ChemFrag enables the annotation of MS n spectra by combining a rule-based fragmentation and a semiempirical quantum chemical approach. In this study, the rule set was extended by 31 cleavage rules and 12 rearrangement rules and used for the interpretation of ESI(+)-MS n spectra of antibiotics, pesticides, and natural products as well as their structural analogs. The fragmentation pathways predicted by ChemFrag for compounds such as 17β-estradiol were confirmed by a comparison with pathways published in other studies. In addition, the annotations were compared with those of the programs MetFrag and CFM-ID , for example, with regard to the number and intensity of annotated fragment ions. Our experiments show that ChemFrag provides reliable and in some cases chemically more realistic annotations for the fragment ions of the investigated compounds. Thus, ChemFrag is a helpful addition to the established in silico methods for the interpretation of ESI(+)-MS n spectra.

Mitapivat is a novel, first-in-class, allosteric activator of pyruvate kinase enzyme. It has been approved by the US FDA in February 2022 for disease modifying treatment of haemolytic anaemia in adults. In the current study, the in vivo metabolites of mitapivat in the rat model were identified using quadrupole-time-of-flight mass spectrometry. A total 20 metabolites were identified, out of which nine metabolites were found to be novel and reported first time in the literature. The study also further refined the chemical structures of some of the reported metabolites. Oxidation, N-dealkylation, oxidation followed by dehydrogenation, and hydrolysis were the major Phase I metabolic pathways of mitapivat. The chief Phase II metabolism pathway was glucuronide conjugation of oxidised and amide hydrolysed metabolites of mitapivat.